Component maintenance prediction system with behavior modeling

ABSTRACT

A method, apparatus, system for managing a maintenance for a component in a vehicle. Sensor data is received for the vehicle. Target parameters are predicted using behavior machine learning models trained using first training data to predict the target parameters for a normal behavior of the component operating in a tolerance, wherein the target parameters characterize behavior of the component. Prediction metrics are determined from predicted values for the target parameters predicted by behavior machine learning models and actual values for the target parameters. Whether the component will fall out of the tolerance after a time period is predicted using the prediction metrics and a maintenance machine learning model trained using second training data to predict whether the maintenance is needed for the component, wherein the second training data comprises historical prediction metrics determined for the target parameters.

BACKGROUND INFORMATION 1. Field

The present disclosure relates generally to an improved aircraftmanagement system and in particular, to implement predictive aircraftmaintenance.

2. Background

Aircraft maintenance involves performing various operations on anaircraft to ensure continued desired operation of the aircraft oraircraft component. The operations can include inspection, replacement,reworking inconsistencies in components, or other operations thatmaintain compliance with airworthiness directives and maintenancestandards.

Aircraft maintenance is often performed on a scheduled basis. In somecases, unscheduled aircraft maintenance can occur when a particularcomponent no longer performs as desired. Unscheduled aircraftmaintenance can be challenging depending on deployment location of anaircraft and the availability of spare components in differentoperational regions. Current maintenance systems rely on reactionarymaintenance schedules for unscheduled maintenance. For example, aircraftsuch as rotorcraft may be essentially grounded while replacementcomponents and repairs are requested, ordered, and then delivered to thelocation of the rotorcraft. This type of maintenance can increase thetime that an aircraft is out of service.

Therefore, it would be desirable to have a method and apparatus thattake into account at least some of the issues discussed above, as wellas other possible issues. For example, it would be desirable to have amethod and apparatus that overcome a technical problem with moreaccurately scheduling aircraft maintenance.

SUMMARY

An embodiment of the present disclosure provides a method for managing amaintenance for a component in a vehicle. Behavior machine learningmodels are trained using first training data to output predicted valuesfor target parameters for a normal behavior of component operating in atolerance. Each behavior machine learning model in the behavior machinelearning models predicts a target parameter in the target parameters forthe component. Historical prediction metrics are determined from thepredicted values for the target parameters predicted by behavior machinelearning models in response to receiving historical sensor data andactual values for the target parameters for the component. A maintenancemachine learning model is trained using second training data to predictwhether the maintenance is needed for the component, wherein the secondtraining data comprises the historical prediction metrics determined forthe target parameters, wherein the maintenance machine learning modeloutputs a prediction as to whether the component will become out of thetolerance after a time period. Whether the maintenance is needed for thecomponent is determined using sensor data for the component, predictionmetrics determined from the predicted values for the target parametersoutput by the behavior machine learning models and actual values for thetarget parameters, and the maintenance machine learning model.

Another embodiment of the present disclosure provides a method formanaging a maintenance for a component in a vehicle. Sensor data isreceived for the vehicle. Target parameters are predicted using behaviormachine learning models trained using first training data to predict thetarget parameters for a normal behavior of the component operating in atolerance, wherein the target parameters characterize behavior of thecomponent. Prediction metrics are determined from predicted values forthe target parameters predicted by behavior machine learning models andactual values for the target parameters. Whether the component will fallout of the tolerance after a time period is predicted using theprediction metrics and a maintenance machine learning model trainedusing second training data to predict whether the maintenance is neededfor the component, wherein the second training data comprises historicalprediction metrics determined for the target parameters.

Yet an embodiment of the present disclosure provides a vehiclemanagement system comprising a computer system and a maintenance managerin the computer system. The maintenance manager is configured to trainbehavior machine learning models using first training data to outputpredicted values for target parameters for a normal behavior of acomponent operating in a tolerance. Each behavior machine learning modelin the behavior machine learning models predicts a target parameter inthe target parameters for the component. The maintenance manager isconfigured to determine historical prediction metrics from the predictedvalues for the target parameters predicted by behavior machine learningmodels in response to receiving historical sensor data and actual valuesfor the target parameters for the component. The maintenance manager isconfigured to train a maintenance machine learning model using secondtraining data to predict whether a maintenance is needed for thecomponent. The second training data comprises the historical predictionmetrics determined for the target parameters, wherein the maintenancemachine learning model outputs a prediction as to whether the componentwill become out of the tolerance after a time period. The maintenancemanager is configured to determine whether maintenance is needed for thecomponent using sensor data for the component, the prediction metricsdetermined from the predicted values for the target parameters output bythe behavior machine learning models and the actual values for thetarget parameters, and the maintenance machine learning model.

In still another embodiment of the present disclosure provides a vehiclemanagement system comprising a computer system and a maintenance managerin the computer system. The maintenance manager is configured to receivesensor data for the vehicle. The maintenance manager is configured topredict target parameters using behavior machine learning models trainedusing first training data to predict the target parameters for a normalbehavior of a component operating in a tolerance. The target parameterscharacterize behavior of the component. The maintenance manager isconfigured to determine prediction metrics from predicted values for thetarget parameters predicted by behavior machine learning models andactual values for the target parameters. The maintenance manager isconfigured to predict whether the component will fall out of thetolerance after a time period using the prediction metrics and amaintenance machine learning model trained using second training data topredict whether the maintenance is needed for the component. The secondtraining data comprises historical prediction metrics determined for thetarget parameters and actual values for the target parameters.

The features and functions can be achieved independently in variousembodiments of the present disclosure or may be combined in yet otherembodiments in which further details can be seen with reference to thefollowing description and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features believed characteristic of the illustrativeembodiments are set forth in the appended claims. The illustrativeembodiments, however, as well as a preferred mode of use, furtherobjectives and features thereof, will best be understood by reference tothe following detailed description of an illustrative embodiment of thepresent disclosure when read in conjunction with the accompanyingdrawings, wherein:

FIG. 1 is a pictorial representation of a network of data processingsystems in which illustrative embodiments may be implemented;

FIG. 2 is a block diagram of a maintenance environment in accordancewith an illustrative embodiment;

FIG. 3 is an illustration of a block diagram for training machinelearning models in accordance with an illustrative embodiment;

FIG. 4 is illustration of a block diagram for predicting whethermaintenance is needed for a component in accordance with an illustrativeembodiment;

FIG. 5 is an illustration of a flowchart of a process for managing amaintenance for a component in a vehicle in accordance with anillustrative embodiment;

FIG. 6 is an illustration of a flowchart of a process for determiningwhether maintenance is needed for a component in accordance with anillustrative embodiment;

FIG. 7 is an illustration of a flowchart of a process for managing amaintenance for a component in a vehicle in accordance with anillustrative embodiment;

FIG. 8 is an illustration of a flowchart of process for selectingparameters in accordance with an illustrative embodiment;

FIG. 9 is an illustration of a flowchart of process for selectingparameters in accordance with an illustrative embodiment;

FIG. 10 is an illustration of a block diagram of a data processingsystem in accordance with an illustrative embodiment;

FIG. 11 is an illustration of an aircraft manufacturing and servicemethod in accordance with an illustrative embodiment;

FIG. 12 is an illustration of a block diagram of an aircraft in which anillustrative embodiment may be implemented; and

FIG. 13 is an illustration of a block diagram of a product managementsystem is depicted in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The illustrative embodiments recognize and take into account one or moredifferent considerations as described below. For example, theillustrative embodiments recognize and take into account thatmaintenance for aircraft or other platforms can be easier to managehaving knowledge of impending changes in component performance thatresult in undesired aircraft performance. For example, the illustrativeembodiments recognize and take into account that the undesired aircraftperformance may be, for example, reduction in fuel efficiency, flightenvelopes, maximum altitude, or aircraft speed.

Accurately knowing the remaining useful life (RUL) of the componentsenables procuring components ahead of time and enables the allocation ofresources for service and repair. The illustrative embodiments recognizeand take account that this knowledge can be especially useful when theavailability of spare components may be difficult to obtain in differentregions of the world.

The illustrative embodiments recognize and take into account thatknowing when the component will no longer operate as desired is neededfor planning the replacement of the component. This knowledge canprovide increased lead times for allocating resources such as personnel,replacement components, or both personnel and replacement. Thus,illustrative embodiments recognize and take into account that accuratelyknowing when a component will no longer operate as desired can result inreducing the resource burden for maintaining aircraft. Further, scheduleinterruption of flights can be reduced or avoided such that delays forpassengers and operation cost for crews and airports can be reduced oravoided.

Aircraft are equipped with numerous sensors that can record data aboutaircraft operations during, before, and after flights. This data can beanalyzed to determine when maintenance may be needed. For example, anaircraft may have tens of thousands of sensors that can generate sensordata periodically such as once per second.

This data can be used to make predictions as to when components will nolonger performs as desired. Further, each component can interact withmany different components in an aircraft. The dynamics of componentinteraction can be fairly complex making it challenging to find simpleformulas or decision rules from physical laws or experiments to makeaccurate assessments as to when maintenance is needed.

Additionally, current maintenance systems have difficulty predictingwhen a component replacement is needed with the amount of data generatedby aircraft fleets. The amount of data can quickly overwhelm maintenancesystems. For example, amount of data for a small aircraft fleet can bein petabytes. These systems can use engineers, service engineers, designengineers, and other subject matter experts (SMEs) to pare down theamount of data.

Further, this information used is based on the understanding that thehuman operators have for a particular system. Also, this type ofselection of information for predictions can be time-consuming andsubject to error.

For example, subject matter experts can miss helpful parameters used inpredicting when a component needs maintenance. A subject matter expertmay focus on a single system in which a component is located withoutpaying attention to how other systems can impact the maintenance needsfor the component. As result, data from these other systems may not betaken into account in the analysis for determining when maintenance isneeded for the component.

In recognizing these and other considerations, illustrative examplesprovide a method, apparatus, system, and computer program product forpredicting the state of components for aircraft. The illustrativeexamples can be applied to a fleet of aircraft and based on current andfuture conditions. The illustrative examples can predict maintenanceneeds based on initial assessment of features and sensor dataparameters. The parameters used can be selected based on impact on theprediction. For example, parameters can be selected as those having themost impact on the prediction. Further, feature data can be selectedtemporarily and according to conditions. These conditions can be airtemperature, air density, and other conditions. The prediction of when acomponent will no longer operate within a tolerance can be predictedusing a subset of the sensor data based on time and conditions foroperations.

With reference now to the figures and, in particular, with reference toFIG. 1 , a pictorial representation of a network of data processingsystem is depicted in which illustrative embodiments may be implemented.Network data processing system 100 is a network of computers in whichthe illustrative embodiments may be implemented. Network data processingsystem 100 contains network 102, which is the medium used to providecommunications links between various devices and computers connectedtogether within network data processing system 100. Network 102 mayinclude connections, such as wire, wireless communication links, orfiber optic cables.

In the depicted example, server computer 104 and server computer 106connect to network 102 along with storage unit 108. In addition, clientdevices 110 connect to network 102. As depicted, client devices 110include client computer 112, client computer 114, and client computer116. Client devices 110 can be, for example, computers, workstations, ornetwork computers. In the depicted example, server computer 104 providesinformation, such as boot files, operating system images, andapplications to client devices 110. Further, client devices 110 can alsoinclude other types of client devices such as mobile phone 118, tabletcomputer 120, and smart glasses 122. In this illustrative example,server computer 104, server computer 106, storage unit 108, and clientdevices 110 are network devices that connect to network 102 in whichnetwork 102 is the communications media for these network devices. Someor all of client devices 110 may form an Internet of things (IoT) inwhich these physical devices can connect to network 102 and exchangeinformation with each other over network 102.

Client devices 110 are clients to server computer 104 in this example.Network data processing system 100 may include additional servercomputers, client computers, and other devices not shown. Client devices110 connect to network 102 utilizing at least one of wired, opticalfiber, or wireless connections.

Program instructions located in network data processing system 100 canbe stored on a computer-recordable storage media and downloaded to adata processing system or other device for use. For example, programinstructions can be stored on a computer-recordable storage media onserver computer 104 and downloaded to client devices 110 over network102 for use on client devices 110.

In this illustrative example, client computer 112 is located in aircraft130, and client computer 114 is located in aircraft 132. In thisdepicted example, other computers and data processing devices arepresent in aircraft 130 and aircraft 132 in addition to client computer112 in aircraft 130 and client computer 114. In other illustrativeexamples, client computer 112 and client computer 114 can be locatedexternal to aircraft 130 in aircraft 132 but are in communication withthese aircraft.

In the depicted example, network data processing system 100 is theInternet with network 102 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers consisting of thousands of commercial, governmental,educational, and other computer systems that route data and messages. Ofcourse, network data processing system 100 also may be implemented usinga number of different types of networks. For example, network 102 can becomprised of at least one of the Internet, an intranet, a local areanetwork (LAN), a metropolitan area network (MAN), or a wide area network(WAN). FIG. 1 is intended as an example, and not as an architecturallimitation for the different illustrative embodiments.

As used herein, “a number of” when used with reference to items, meansone or more items. For example, “a number of different types ofnetworks” is one or more different types of networks.

Further, the phrase “at least one of,” when used with a list of items,means different combinations of one or more of the listed items can beused, and only one of each item in the list may be needed. In otherwords, “at least one of” means any combination of items and number ofitems may be used from the list, but not all of the items in the listare required. The item can be a particular object, a thing, or acategory.

For example, without limitation, “at least one of item A, item B, oritem C” may include item A, item A and item B, or item B. This examplealso may include item A, item B, and item C or item B and item C. Ofcourse, any combinations of these items can be present. In someillustrative examples, “at least one of” can be, for example, withoutlimitation, two of item A; one of item B; and ten of item C; four ofitem B and seven of item C; or other suitable combinations.

In this illustrative example, maintenance manager 134 can predict whenmaintenance is needed for aircraft 130 and aircraft 132 using behaviormachine learning models 138 and maintenance machine learning model 140.More specifically, maintenance manager 134 can use these machinelearning models to predict when components in aircraft 130 and aircraft132 will no longer function within a tolerance. In these illustrativeexamples, this prediction can be made for a future window of time inwhich the component will no longer operate within the tolerance.

This type of performance can be characterized as not operating withinthe tolerance. A component can be considered to be out of the tolerancewhen the component no longer works or functions, the performance of thecomponent is not within a selected threshold, or the performance can bebased on some other measurement.

This illustrative example, client computer 112 sends sensor data 142from aircraft 130 to maintenance manager 134 over network 102. Sensordata 142 for aircraft 130 can comprise data generated periodically byaircraft 130. For example, data can be generated every second or at arate of 1 Hz.

As depicted, maintenance manager 134 inputs sensor data 142 intobehavior machine learning models 138. Behavior machine learning models138 output predictions for target parameters 144 for a component inaircraft 130. In this illustrative example, each behavior machinelearning model in behavior machine learning models 138 predicts a targetparameter in target parameters 144 for the component.

The prediction of target parameters 144 is a process by maintenancemanager 134 to generate metrics 143 for target parameters 144. Metrics143 are input into maintenance machine learning model 140. In response,maintenance machine learning model 140 outputs a prediction as towhether maintenance will be needed for the component within a timewindow. This time window can be, for example, a period of one weekbeginning from the timestamp for sensor data 142.

As another example, client computer 114 can send sensor data 150 fromaircraft 132 to maintenance manager 134. In this example, maintenancemanager 134 can use behavior machine learning models 138 and maintenancemachine learning model 140 to generate a prediction of whether acomponent in aircraft 132 will need maintenance within a time window.For example, the component can be of the same type or model as thecomponent in aircraft 130.

In this example, the prediction for a component of the same type inaircraft 132 can be made using the same behavior machine learning modelsand maintenance machine learning model. In another example, additionalbehavior machine learning models and another maintenance machinelearning model specifically trained for predicting maintenance for thatcomponent in aircraft 132 can be used.

When predictions are made for additional types of components, additionalbehavior machine learning models and maintenance machine learning modelstrained for those additional components can be used. In yet otherillustrative examples, different machine learning models can be trainedfor the same type of component in different locations within the sameaircraft.

With reference now to FIG. 2 , a block diagram of a maintenanceenvironment is depicted in accordance with an illustrative embodiment.In this illustrative example, maintenance environment 200 includescomponents that can be implemented in hardware such as the hardwareshown in network data processing system 100 in FIG. 1 .

In this illustrative example, vehicle management system 202 inmaintenance environment 200 can operate to manage maintenance 204 forvehicle 206. Vehicle 206 can take a number of different forms. Forexample, vehicle 206 can be selected from a group comprising a mobileplatform, an aircraft, a commercial airplane, a tilt-rotor aircraft, atilt wing aircraft, a vertical takeoff and landing aircraft, anelectrical vertical takeoff and landing vehicle, a personal air vehicle,a surface ship, a tank, a personnel carrier, a train, a spacecraft, asubmarine, a bus, and an automobile.

For example, vehicle management system 202 can operate to predictmaintenance 204 for component 208 in vehicle 206. Component 208 can takea number of different forms. For example, component 208 can be selectedfrom a group comprising a door, a skin panel, a wiring harness, afaster, a fairing, an engine, and auxiliary power unit (APU), a fan, abracket, a brace, a seal, a sensor, a window, a switch, a lever, achair, a monument, or other suitable type of component used in vehicle206.

As depicted, vehicle management system 202 comprises computer system 210and maintenance manager 212. In this example, maintenance manager 212 islocated in computer system 210.

Maintenance manager 212 can be implemented in software, hardware,firmware, or a combination thereof. When software is used, theoperations performed by maintenance manager 212 can be implemented inprogram code configured to run on hardware, such as a processor unit.When firmware is used, the operations performed by maintenance manager212 can be implemented in program code and data and stored in persistentmemory to run on a processor unit. When hardware is employed, thehardware can include circuits that operate to perform the operations inmaintenance manager 212.

In the illustrative examples, the hardware can take a form selected fromat least one of a circuit system, an integrated circuit, an applicationspecific integrated circuit (ASIC), a programmable logic device, or someother suitable type of hardware configured to perform a number ofoperations. With a programmable logic device, the device can beconfigured to perform the number of operations. The device can bereconfigured at a later time or can be permanently configured to performthe number of operations. Programmable logic devices include, forexample, a programmable logic array, a programmable array logic, a fieldprogrammable logic array, a field programmable gate array, and othersuitable hardware devices. Additionally, the processes can beimplemented in organic components integrated with inorganic componentsand can be comprised entirely of organic components excluding a humanbeing. For example, the processes can be implemented as circuits inorganic semiconductors.

Computer system 210 is a physical hardware system and includes one ormore data processing systems. When more than one data processing systemis present in computer system 210, those data processing systems are incommunication with each other using a communications medium. Thecommunications medium can be a network. The data processing systems canbe selected from at least one of a computer, a server computer, a tabletcomputer, or some other suitable data processing system.

As depicted, computer system 210 includes a number of processor units214 that are capable of executing program instructions 216 implementingprocesses in the illustrative examples. As used herein a processor unitin the number of processor units 214 is a hardware device and iscomprised of hardware circuits such as those on an integrated circuitthat respond and process instructions and program code that operate acomputer. When a number of processor units 214 execute programinstructions 216 for a process, the number of processor units 214 is oneor more processor units that can be on the same computer or on differentcomputers. In other words, the process can be distributed betweenprocessor units on the same or different computers in a computer system.Further, the number of processor units 214 can be of the same type ordifferent type of processor units. For example, a number of processorunits can be selected from at least one of a single core processor, adual-core processor, a multi-processor core, a general-purpose centralprocessing unit (CPU), a graphics processing unit (GPU), a digitalsignal processor (DSP), or some other type of processor unit.

In this illustrative example, in predicting whether maintenance 204 isneeded, maintenance manager 212 can make this prediction using machinelearning models 218. A machine learning model in machine learning models218 is a type of artificial intelligence model that can learn withoutbeing explicitly programmed. A machine learning model can learn basedtraining data input into the machine learning model. The machinelearning model can learn using various types of machine learningalgorithms. The machine learning algorithms include at least one of asupervised learning, and unsupervised learning, a feature learning, asparse dictionary learning, an anomaly detection, a reinforcementlearning, a recommendation learning, or other types of learningalgorithms.

Machine learning models 218 can be implemented using various types ofmachine learning model architectures. Examples of machine learningmodels that can be used include an artificial neural network, a decisiontree, a support vector machine, a linear regression machine learningmodel, a random forest learning model, a Bayesian network, a geneticalgorithm, and other types of models. These machine learning models canbe trained using data and process additional data to provide a desiredoutput.

As depicted, machine learning models 218 comprises behavior machinelearning models 220 and maintenance machine learning model 222. In thisillustrative example, behavior machine learning models 220 are trainedto predict normal behavior of component 208. In one illustrativeexample, behavior machine learning models 220 can be implemented usingregression machine learning models.

Maintenance machine learning model 222 is trained to determine whencomponent 208 no longer operates as desired in vehicle 206. Thisundesired operation can be component 208 not operating at all or notoperating with a desired performance level. Machine learning model 222can also be implemented using a classification machine learning model.

In this illustrative example, the training of machine learning models218 is performed in a manner that provides improved prediction ofmaintenance 204 for component 208 as compared to other techniquesincluding those using machine learning models. In this illustrativeexample, the training is performed in two stages.

As depicted, maintenance manager 212 trains behavior machine learningmodels 220 using first training data 224 to output predicted values 226for target parameters 228 for a normal behavior 230 of component 208operating in tolerance 232. In the illustrative example, targetparameters 228 are parameters that characterize behavior of component208. If component 208 is an air compressor, target parameters 228 canbe, for example, rotation speed, output temperature, pressure, air massflow rate, and other parameters that characterize how the air compressoroperates.

Other parameters may characterize the behavior other components forother systems in vehicle 206. In some cases, a target parameter cancharacterize the behavior of more than one component.

With the training, each behavior machine learning model in the behaviormachine learning models 220 predicts a target parameter in the targetparameters 228 for the component 208. In other words, each behaviormachine learning model in behavior machine learning models 220 output apredicted value for a particular target parameter. As a result, eachbehavior machine learning model is trained for different targetparameter in target parameters 228 from other behavior machine learningmodels.

After training of behavior machine learning models 220, maintenancemanager 212 uses the machine learning models to generate second trainingdata 244 for use in training maintenance machine learning model 222. Inthis illustrative example, maintenance manager 212 controls thesemachine learning models to output predicted values 226 for targetparameters 228. Maintenance manager 212 determines historical predictionmetrics 234 from predicted values 226 for target parameters 228predicted by behavior machine learning models 220 using historicalsensor data 236. This prediction is performed in response receivinghistorical sensor data 236 and actual values 238 for target parameters228 for the component 208.

In this example, historical sensor data 236 comprises sensor data 240previously received from sensor system 242 for vehicle 206. Sensorsystem 242 comprises sensors that detect information about components invehicle 206 and the environment in and around vehicle 206.

In this illustrative example, predicted values 226 for target parameters228 are generated using historical sensor data 236 for target parameters228. Predicted values 226 for target parameters 228 are compared toactual values 238 in historical sensor data 236 for target parameters228 to determine historical prediction metrics 234. In other words,historical prediction metrics 234 can describe statistics aboutpredicted values 226 and actual values 238 for target parameters 228.

In this illustrative example, historical prediction metrics 234 formsecond training data 244. Maintenance manager 212 trains maintenancemachine learning model 222 using second training data 244 to predictwhether the maintenance is needed for the component. In this example,second training data 244 comprises historical prediction metrics 234determined for target parameters 228. As trained, maintenance machinelearning model 222 outputs prediction 246 that component 208 is becomingout of tolerance 232 after time period 248. In this illustrativeexample, prediction 246 can take a number of different forms. Prediction246 can be in a form of a true or false statement. Further, thestatement can also include a probability of the accuracy of the true orfalse statement.

Maintenance manager 212 can determine whether maintenance 204 is neededfor component 208 using sensor data 240 for component 208, predictionmetrics 250 determined from predicted values 226 for target parameters228 outputted by behavior machine learning models 220 and actual values238 for target parameters 228, and maintenance machine learning model222.

Maintenance manager 212 can perform a set of actions 254 based ondetermining whether maintenance 204 is needed. The set of actions 254can take a number of different forms. For example, the set of actionscan be selected from at least one of scheduling maintenance 204, loggingprediction 246, sending a notification to a maintenance facility,generating an alert, ordering a replacement component, or other suitableactions.

In one illustrative example, one or more technical solutions are presentthat overcome a technical problem with determining when maintenance isneeded for components. As a result, one or more technical solutions canprovide a technical effect using a configuration of machine learningmodels to more accurately determine whether a component will become outof a tolerance within a time period. This prediction can provide time toschedule maintenance prior to undesired performance of the components.This maintenance can include at least one of inspection, routinemaintenance, part replacement, or other suitable type of maintenance.

Computer system 210 can be configured to perform at least one of thesteps, operations, or actions described in the different illustrativeexamples using software, hardware, firmware, or a combination thereof.As a result, computer system 210 operates as a special purpose computersystem in which maintenance manager 212 in computer system 210 enablesmore accurately predicting whether a component will no longer operatewithin tolerance 232 within a period of time. In particular, maintenancemanager 212 transforms computer system 210 into a special purposecomputer system as compared to currently available general computersystems that do not have maintenance manager 212.

In the illustrative example, the use of maintenance manager 212 incomputer system 210 integrates processes into a practical applicationfor a method of managing maintenance for a vehicle. Further, maintenancemanager 212 using machine learning models 218 can predict whethercomponent 208 will no longer operate within tolerance 232 in time period248. With the amount of sensor data and analysis required, this type ofprediction cannot be practically performed by a human operator in timeto determine whether maintenance for a component in vehicle 206.Further, this type of analysis and prediction cannot be performed forthe multitude of components in a vehicle and the number of vehicles thatoperator may have.

With reference next to FIG. 3 , an illustration of a block diagram fortraining machine learning models is depicted in accordance with anillustrative embodiment. In the illustrative examples, the samereference numeral may be used in more than one figure. This reuse of areference numeral in different figures represents the same element inthe different figures.

In the training process, maintenance manager 212 generates firsttraining data 224 from historical sensor data 236. In this particularexample, vehicle 206 is in the form of an aircraft.

In this illustrative example, maintenance manager 212 can select targetparameters 228 from parameters 322. Parameters 322 are parameters forwhich sensor data 240 can be generated by sensor system 242.

Target parameters 228 are identified from parameters 322 as parametersthat characterize the behavior of component 208. In identifying targetparameters 228 from parameters 322, maintenance manager 212 candetermine a correlation between parameters 322 and the behavior ofcomponent 208.

With an initial selection of target parameters 228, the number of targetparameters can be reduced through a number of different operations. Thereduction can be performed to reduce the amount of data and processingresources needed while maintaining a desired level of prediction made bybehavior machine learning models 220.

For example, maintenance manager 212 can determine whether staleparameters 310 are present in target parameters 228. A stale parametercan describe the behavior of component 208 but never changes or does notchange substantially during most flights. These stale parameters 310 canbe removed from target parameters 228 in this example.

Further, in selecting target parameters 228, maintenance manager 212 canalso identify redundant parameters 312 in target parameters 228. Aredundant parameter is a parameter that is highly correlated to one ormore other parameters. Target parameters 228 that are identified asredundant parameters 312 can be removed from target parameters 228.

In the illustrative example, temporal information can be represented ina number of different ways using parameters. For example, one approachinvolves repeating readings of parameters 322 for a given window at eachsampling time. In other cases, temporal information for parameters 322can be aggregated into features 304. Features 304 can be created fromindividual parameters and used with machine learning models that cancombine correlated features correctly. In this manner, larger amount ofdata can be aggregated for training. As a result, first training data224 can be based on at least one of parameters 322, target parameters228, or features 304.

In yet another example, features 304 for behavior machine learningmodels 220 can be augmented others features from engineering hypotheses,such as summary statistics of the ambient conditions, behavior modeltarget parameters, and selected key features over the whole flight orduring different conditions. As another examples, features 304 can beaugmented with time delay from control signals or key events to thedesired conditions. For example, the time it takes for speed to spooldown to zero or increase to target, or for pressure to stabilize.

Features 304 can capture temporal information with the modificationssuch as for maintenance prediction, future flights are not used unlikethe future samples within the same flight for behavior modeling. Flighttime is not uniformly distributed over time (unlike 1 Hz sampling ofparameter values within the same flight). With this situation, wallclock time can be used as moving window size, or number of flights asmoving window size. Missing flights, which happens more often thanmissing samples in a single flight, may create a problem for movingwindow calculations. If the number of missing flights in the window canbe estimated, the missing flight can be a candidate feature in features304.

Further features 304 can be separated into positions, for each positiongroup, and others that are common to all positions. For example, if twocabin air compressors on each side of an aircraft, four positions arepresent. As a result, features can be identified for each of the fourpositions (L1, L2, R1, and R2), for each side (L1 and L2 vs. R1 and R2),for each number (L1 and R1 as inboard, L2 and R2 as outboard). Topredict the maintenance for a specific position (e.g., Left Inboard orL1), features 304 specific to that position (L1), or to a position groupcontaining that position (e.g., CAC Left, or CAC Inboard), can be usedin addition to all common features. As a result, for each targetposition, G+1 prediction models may be present, where G is the number ofposition groups for each target position.

In one illustrative example, first training data 224 for a behaviormachine learning model in the behavior machine learning models 220comprises historical sensor data 236 that has been labeled with targetparameter values 300 for target parameter 306 in target parameters 228to be predicted by the behavior machine learning model, wherein eachbehavior machine learning model is trained to predict a different targetparameter from other behavior machine learning models.

After behavior machine learning models 220 have been trained, thesemachine learning models can output predicted values 226 for targetparameters 228. Each behavior machine learning model in behavior machinelearning models 220 outputs a predicted value in predicted values 226for the target parameter in target parameters for which the behaviormachine learning model was trained.

In this illustrative example, historical sensor data 236 can be inputinto behavior machine learning models 220 to generate predicted values226. Predicted values 226 can be used to generate second training data244. In this illustrative example, maintenance manager 212 analyzespredicted values 226 for target parameters 228 with actual values 238for target parameters 228. Actual values 238 can be obtained fromhistorical sensor data 236.

As depicted, actual values 238 are not included from historical sensordata 236 used for first training data 224. In other words, actual values238 are not included in first training data 224 for use for trainingbehavior machine learning models 220.

This analysis can be performed to obtain historical prediction metrics234. In this illustrative example, historical prediction metrics 234 aregenerated by comparing predicted values 226 to actual values 238. Inother words, historical prediction metrics 234 can describe statisticsabout predicted values 226 and actual values 238 for target parameters228.

These metrics can be determined for each testing flight and each targetparameter. For example, historical prediction metrics 234 can be atleast one of quantiles for the prediction error (true value−predictedvalue); quantiles for the absolute prediction error; root mean squareerror and mean absolute error; or goodness-of-fit, such as R2 score.These metrics can be calculated for an entire flight or phase of flightfor an aircraft in this example.

In the illustrative example, prediction metrics can be calculated forthe whole flight, a phase of flight, or for different conditions. Forexample, prediction metrics can be calculated for a single flight phase,such as taxi out or cruise and flight condition sub-segments, such aswithin initial climb and descent flight phases, where conditions changerapidly. As another example, prediction metrics can be calculated to anoperation mode of component 208. Prediction metrics can be calculatedfor an operation condition of component 208. For example, altitude,pressure, and temperature can be combined into different conditions forcomponent 208, such as a cabin air compressor. Prediction metrics can becalculated for control switch states, inlet, and outlet valve positions(in discrete bins), and auxiliary upstream and downstream fan speeds (indiscrete bins).

Maintenance manager 212 creates second training data 244 using theaddiction metrics. In this illustrative example, second training data244 comprises historical prediction metrics 234 and labels 308,indicating whether component 208 was out of tolerance 232.

In this illustrative example, whether component 208 was out of tolerance232 can be determined based on maintenance records. For example, if acomponent replacement occurs, sensor data sufficiently long (for examplemore than 1 month) before that date can be labeled as “normal” with thecomponent being in in tolerance 232. The historical prediction metricsbased on sensor data close and prior to the component replacement date,for example within 1 month, can be labeled as being “close to out oftolerance”. In other illustrative examples, other types and numbers oflabels can be used depending on the ability to analyze the historicalsensor data 236 and maintenance records to determine the operatingcondition of component 208.

In this example, prediction 246 can be the amount of time until a nextout of tolerance condition from the flight time. The time can be day,weeks, or some other time period.

Second training data 244 can be used to train maintenance machinelearning model 222 to output prediction 246 that predicts whethercomponent 208 will be in tolerance 232 or out of tolerance 232 aftertime period 248.

As result, the predictions using the behavior machine learning models220 and maintenance machine learning model 222 can be for at least oneof a phase of a flight, a taxi out, a takeoff, a climb, a cruise, adescent, a landing, an entire flight of the aircraft, a mode ofoperation of the aircraft, or a target range of control condition forthe aircraft.

With reference next to FIG. 4 , illustration of a block diagram forpredicting whether maintenance is needed for component is depicted inaccordance with an illustrative embodiment. After training behaviormachine learning models 220 and maintenance machine learning model 222,these machine learning models can now be used predict whether component208 will be out of tolerance 232 after time period 248. With thisprediction, component 208 should not be out of tolerance 232 prior tothe expiration of time.

This type of prediction can be made with maintenance machine learningmodel 222 being implemented using a classification machine learningmodel that can be trained to predict another out of tolerance conditionof component 208 within the next week, next month, next quarter, or someother time period.

In determining whether maintenance is needed for component 208,maintenance manager 212 receives sensor data 240 for component 208. Thissensor data can be received from sensor system 242 as sensor data 240 isgenerated during flight or use of vehicle 206 or sensor data 240 can bereceived from sensor system 242 after the flight or use of vehicle 206.

In this illustrative example, maintenance manager 212 removes actualvalues 238 for target parameters 228 in sensor data 240 prior to sendingsensor data 240 into behavior machine learning models 220. In otherwords, sensor data 240 sent into behavior machine learning models 220does not include actual values 238 for target parameters 228.

Maintenance manager 212 sends sensor data 240 into behavior machinelearning models 220. In response, maintenance manager 212 receivespredicted values 226 for target parameters 228 from behavior machinelearning models 220.

Maintenance manager 212 determines prediction metrics 250 from predictedvalues 226 for target parameters 228 output from behavior machinelearning models 220 for sensor data 240 and actual values 238. Thisdetermination made by maintenance manager 212 can be performed bycomparing actual values 238 to predicted values 226 for targetparameters 228. In this example, prediction metrics 250 are generatedfor each target parameter.

With prediction metrics 250, maintenance manager 212 sends predictionmetrics 250 into maintenance machine learning model 222. In response,maintenance manager 212 receives prediction 246 of whether maintenanceis needed for component 208 as an output from maintenance machinelearning model 222.

With prediction 246, maintenance manager 212 can perform a set ofactions 254. The set of actions can be selected from at least one ofscheduling maintenance 204, logging prediction 246, sending anotification to a maintenance facility, generating an alert, ordering areplacement component, or other suitable actions.

The illustration of maintenance environment 200 in FIG. 2 is not meantto imply physical or architectural limitations to the manner in which anillustrative embodiment may be implemented. Other components in additionto or in place of the ones illustrated may be used. Some components maybe unnecessary. Also, the blocks are presented to illustrate somefunctional components. One or more of these blocks may be combined,divided, or combined and divided into different blocks when implementedin an illustrative embodiment.

For example, although behavior machine learning models 220 andmaintenance machine learning model 222 is described as being implementedusing a classification machine learning model, other implementations canuse other types of machine learning models.

As another example, maintenance manager 212 can be configured to performpredictions from multiple components within vehicle 206. Further,prediction 246 can be performed for component 208 in particularlocation. For example, prediction 246 can be for component 208 in theform of an aircraft engine on the right side of an aircraft. In thiscase, machine learning models 218 can be trained using sensor data 240from engines on the right side of aircraft. Further, the training can beperformed for a particular aircraft, fleet, or airline.

Turning next to FIG. 5 , an illustration of a flowchart of a process formanaging a maintenance for a component in a vehicle is depicted inaccordance with an illustrative embodiment. The process in FIG. 5 can beimplemented in hardware, software, or both. When implemented insoftware, the process can take the form of program code that is run byone of more processor units located in one or more hardware devices inone or more computer systems. For example, the process can beimplemented in maintenance manager 212 in computer system 210 in FIG. 2.

The process begins by training behavior machine learning models usingfirst training data to output predicted values for target parameters fora normal behavior of component operating in tolerance (operation 500).In this operation, each behavior machine learning model in the behaviormachine learning models predicts a target parameter in the targetparameters for the component.

The process determines historical prediction metrics from the predictedvalues for the target parameters predicted by behavior machine learningmodels in response to receiving historical sensor data and actual valuesfor the target parameters for the component (operation 502). The processtrains a maintenance machine learning model using second training datato predict whether the maintenance is needed for the component(operation 504). In operation 504, the second training data comprisesthe historical prediction metrics determined for the target parameters.The maintenance machine learning model trained in operation 504, outputsa prediction as to whether the component will become out of toleranceafter a time period.

The process determines whether the maintenance is needed for thecomponent using sensor data for the component, prediction metricsdetermined from the predicted values for the target parameters output bythe behavior machine learning models and actual values for the targetparameters, and the maintenance machine learning model (operation 506).The process terminates thereafter.

Turning next to FIG. 6 , an illustration of a flowchart of a process fordetermining whether maintenance is needed for component is depicted inaccordance with an illustrative embodiment. The operations in thisflowchart are examples of an indication for operation 506 in FIG. 5 .

The process begins by sending sensor data for the component into thebehavior machine learning models (operation 600). The process receivesthe predicted values for the target parameters from the behavior machinelearning models (operation 602). The process determines the predictionmetrics from the predicted values for the target parameters output fromthe behavior machine learning model for the sensor data and the actualvalues for the target parameters in the sensor data (operation 604).

The process sends the prediction metrics into the maintenance machinelearning model (operation 606). The process receives a prediction ofwhether the maintenance is needed for the component from maintenancemachine learning model (operation 608). The process terminatesthereafter.

Turning next to FIG. 7 , an illustration of a flowchart of a process formanaging a maintenance for a component in a vehicle is depicted inaccordance with an illustrative embodiment. The process in FIG. 7 can beimplemented in hardware, software, or both. When implemented insoftware, the process can take the form of program code that is run byone of more processor units located in one or more hardware devices inone or more computer systems. For example, the process can beimplemented in maintenance manager 212 in computer system 210 in FIG. 2.

The process begins by receiving sensor data for the vehicle (operation700). The process predicts target parameters using behavior machinelearning models trained using first training data to predict the targetparameters for a normal behavior of the component operating intolerance, wherein the target parameters characterize behavior of thecomponent (operation 702). The process determines prediction metricsfrom predicted values for the target parameters predicted by behaviormachine learning models and actual values for the target parameters(operation 704).

The process predicts predicting whether the component will fall out oftolerance after a time period using the prediction metrics and amaintenance machine learning model trained using second training data topredict whether the maintenance is needed for the component (operation706). The process terminates thereafter. The second training data usedto train the maintenance learning machine model training data compriseshistorical prediction metrics determined for the target parameters.

Turning to FIG. 8 , of a flowchart of process for selecting parametersis depicted in accordance with an illustrative embodiment. The processillustrated in this figure can be implemented in hardware, software, orboth. When implemented in software, the process can take the form ofprogram code that is run by one of more processor units located in oneor more hardware devices in one or more computer systems. For example,the process can be implemented in maintenance manager 212 in computersystem 210 in FIG. 2 . Process can be used to select parameters fortraining behavior machine learning models to predict target parameters.

The process begins by setting P to be the set of set of all availableparameters, except stale, redundant, or absolute time parameters(operation 800). The process estimates memory usage for each sample thathave values and derives features that use P (operation 802). Inoperation 802, A sample is a value taken at a given time for aparameter. The sample can be a time-value pair. When many parameters aresampled and recorded at the same time, a sample can be the list ofvalues for these parameters at a given time.

The process loads the maximum number of random flights one at a time(operation 804). In operation 804, features can be calculated, andsubsamples of features can be identified using a sampling ratio ρ. Thesubsamples can be a randomly selected subset of the samples.

In this depicted example, in determining the maximum number of flights,memory is reserved for building and storing the model. For example, touse a Random Forest regression with 100 trees, the number of nodes foreach tree may be limited to 100,000. This choice may result in the wholeRandom Forest regression model using about 12 GB memory. Operation 804can be parallelized by partitioning the random flights and assigningeach parallel task load a subset of the flights for processing.

The process uses K-fold cross-validation method to train K models foreach of the T target parameters from the sub-sampled features calculatedpreviously (operation 806). Operation 806 can be parallelized using apool of K*T machine learning tasks. In this example, T is the targetparameters multiplied by K, which is the folds. The folds are the numberof groups that the data is to be split into. For each target parameterand each fold, a separate training task is used.

The process sets P′ as the set of parameters used to derive a feature(operation 808). In operation 808, this set includes parameters in thelist of top M1 features for any of the K models, parameters that appearin the list of top M2 features for all of the K models. The parametersin P′ can also be parameters that have a feature importance of at leastI1 for any of the K models and parameters that have feature importanceof at least I2 for all of the K models. In this example, M1, M2, I1, andI2 are values hyperparameters in the machine learning models. Forexample, M2 can be 5, 10 or some other number of features. The valuescan be defined by a user or a meta-learning or hyper tuning process.

A determination is made as to whether P is equal to P′ (operation 810).If P is not equal to P′, the process sets P as P′ (operation 812) andthe process returns to operation 802. Otherwise, the process terminates.

During each iteration of the process in FIG. 8 , as the number ofparameters is reduced, the number of random flights can be increased sothat the Random Forest regression model can be better generalized andless likely to overfit the training data. For example, using K=10,M1=50, M2=100, I1=0.01, I2=0.001, the process can select about 100parameters from about 2000 recorded parameters for predicting targetparameters for a component such as a cabin air compressor.

Turning to FIG. 9 , of a flowchart of process for selecting parametersis depicted in accordance with an illustrative embodiment. The processillustrated in this figure can be implemented in hardware, software, orboth. When implemented in software, the process can take the form ofprogram code that is run by one of more processor units located in oneor more hardware devices in one or more computer systems. For example,the process can be implemented in maintenance manager 212 in computersystem 210 in FIG. 2 . Process can be used to select features fortraining behavior machine learning models such as Random Forestregression models.

The process begins by setting F to be the set of set of identified fortraining the behavior machine learning models (operation 900). Theprocess estimates memory usage for each sample that have values andderives features that use P (operation 902).

The process loads the maximum number of random flights one at a time(operation 904). In operation 904, features can be calculated, andsubsamples of features can be identified using a sampling ratio ρ.Operation 904 can be parallelized by partitioning the random flights andassigning each parallel task load a subset of the flights forprocessing.

The process uses K-fold cross-validation method to train K models foreach of the T target parameters from the sub-sampled features calculatedpreviously (operation 906). Operation 906 can be parallelized using apool of K*T machine learning tasks.

The process places a feature into F′ if the feature appears in the listof top M1 features for one of the K models; appears in the list of topM2 features for all of the K models; has feature importance of at leastI1 for one of the K models; or has feature importance of at least I2 forone of the K models (operation 908).

A determination is made as to whether F is equal to F′ (operation 910).If F is not equal to F′, the process sets F as F′ (operation 912) andthe process returns to operation 902. Otherwise, the process terminates.

The flowcharts and block diagrams in the different depicted embodimentsillustrate the architecture, functionality, and operation of somepossible implementations of apparatuses and methods in an illustrativeembodiment. In this regard, each block in the flowcharts or blockdiagrams can represent at least one of a module, a segment, a function,or a portion of an operation or step. For example, one or more of theblocks can be implemented as program code, hardware, or a combination ofthe program code and hardware. When implemented in hardware, thehardware can, for example, take the form of integrated circuits that aremanufactured or configured to perform one or more operations in theflowcharts or block diagrams. When implemented as a combination ofprogram code and hardware, the implementation may take the form offirmware. Each block in the flowcharts or the block diagrams can beimplemented using special purpose hardware systems that perform thedifferent operations or combinations of special purpose hardware andprogram code run by the special purpose hardware.

In some alternative implementations of an illustrative embodiment, thefunction or functions noted in the blocks may occur out of the ordernoted in the figures. For example, in some cases, two blocks shown insuccession may be performed substantially concurrently, or the blocksmay sometimes be performed in the reverse order, depending upon thefunctionality involved. Also, other blocks may be added in addition tothe illustrated blocks in a flowchart or block diagram.

Turning now to FIG. 10 , an illustration of a block diagram of a dataprocessing system is depicted in accordance with an illustrativeembodiment. Data processing system 1000 can be used to implement servercomputer 104, server computer 106, client devices 110, in FIG. 1 . Dataprocessing system 1000 can also be used to implement computer system 210in FIG. 2 . In this illustrative example, data processing system 1000includes communications framework 1002, which provides communicationsbetween processor unit 1004, memory 1006, persistent storage 1008,communications unit 1010, input/output (I/O) unit 1012, and display1014. In this example, communications framework 1002 takes the form of abus system.

Processor unit 1004 serves to execute instructions for software that canbe loaded into memory 1006. Processor unit 1004 includes one or moreprocessors. For example, processor unit 1004 can be selected from atleast one of a multicore processor, a central processing unit (CPU), agraphics processing unit (GPU), a physics processing unit (PPU), adigital signal processor (DSP), a network processor, or some othersuitable type of processor. Further, processor unit 1004 can may beimplemented using one or more heterogeneous processor systems in which amain processor is present with secondary processors on a single chip. Asanother illustrative example, processor unit 1004 can be a symmetricmulti-processor system containing multiple processors of the same typeon a single chip.

Memory 1006 and persistent storage 1008 are examples of storage devices1016. A storage device is any piece of hardware that is capable ofstoring information, such as, for example, without limitation, at leastone of data, program code in functional form, or other suitableinformation either on a temporary basis, a permanent basis, or both on atemporary basis and a permanent basis. Storage devices 1016 may also bereferred to as computer-readable storage devices in these illustrativeexamples. Memory 1006, in these examples, can be, for example, arandom-access memory or any other suitable volatile or non-volatilestorage device. Persistent storage 1008 can take various forms,depending on the particular implementation.

For example, persistent storage 1008 may contain one or more componentsor devices. For example, persistent storage 1008 can be a hard drive, asolid-state drive (SSD), a flash memory, a rewritable optical disk, arewritable magnetic tape, or some combination of the above. The mediaused by persistent storage 1008 also can be removable. For example, aremovable hard drive can be used for persistent storage 1008.

Communications unit 1010, in these illustrative examples, provides forcommunications with other data processing systems or devices. In theseillustrative examples, communications unit 1010 is a network interfacecard.

Input/output unit 1012 allows for input and output of data with otherdevices that can be connected to data processing system 1000. Forexample, input/output unit 1012 can provide a connection for user inputthrough at least one of a keyboard, a mouse, or some other suitableinput device. Further, input/output unit 1012 can send output to aprinter. Display 1014 provides a mechanism to display information to auser.

Instructions for at least one of the operating system, applications, orprograms can be located in storage devices 1016, which are incommunication with processor unit 1004 through communications framework1002. The processes of the different embodiments can be performed byprocessor unit 1004 using computer-implemented instructions, which canbe located in a memory, such as memory 1006.

These instructions are program instructions and are also referred to asprogram code, computer usable program code, or computer-readable programcode that can be read and executed by a processor in processor unit1004. The program code in the different embodiments can be embodied ondifferent physical or computer-readable storage media, such as memory1006 or persistent storage 1008.

Program instructions 1018 are located in a functional form oncomputer-readable media 1020 that is selectively removable and can beloaded onto or transferred to data processing system 1000 for executionby processor unit 1004. Program instructions 1018 and computer-readablemedia 1020 form computer program product 1022 in these illustrativeexamples. In the illustrative example, computer-readable media 1020 iscomputer-readable storage media 1024.

Computer-readable storage media 1024 is a physical or tangible storagedevice used to store program instructions 1018 rather than a media thatpropagates or transmits program instructions 1018. Computer readablestorage media 1024, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Alternatively, program instructions 1018 can be transferred to dataprocessing system 1000 using a computer-readable signal media. Thecomputer-readable signal media are signals and can be, for example, apropagated data signal containing program instructions 1018. Forexample, the computer-readable signal media can be at least one of anelectromagnetic signal, an optical signal, or any other suitable type ofsignal. These signals can be transmitted over connections, such aswireless connections, optical fiber cable, coaxial cable, a wire, or anyother suitable type of connection.

Further, as used herein, “computer-readable media 1020” can be singularor plural. For example, program instructions 1018 can be located incomputer-readable media 1020 in the form of a single storage device orsystem. In another example, program instructions 1018 can be located incomputer-readable media 1020 that is distributed in multiple dataprocessing systems. In other words, some instructions in programinstructions 1018 can be located in one data processing system whileother instructions in program instructions 1018 can be located in onedata processing system. For example, a portion of program instructions1018 can be located in computer-readable media 1020 in a server computerwhile another portion of program instructions 1018 can be located incomputer-readable media 1020 located in a set of client computers.

The different components illustrated for data processing system 1000 arenot meant to provide architectural limitations to the manner in whichdifferent embodiments can be implemented. In some illustrative examples,one or more of the components may be incorporated in or otherwise form aportion of, another component. For example, memory 1006, or portionsthereof, can be incorporated in processor unit 1004 in some illustrativeexamples. The different illustrative embodiments can be implemented in adata processing system including components in addition to or in placeof those illustrated for data processing system 1000. Other componentsshown in FIG. 10 can be varied from the illustrative examples shown. Thedifferent embodiments can be implemented using any hardware device orsystem capable of running program instructions 1018.

Illustrative embodiments of the disclosure may be described in thecontext of aircraft manufacturing and service method 1100 as shown inFIG. 11 and aircraft 1200 as shown in FIG. 12 . Turning first to FIG. 11, an illustration of an aircraft manufacturing and service method isdepicted in accordance with an illustrative embodiment. Duringpre-production, aircraft manufacturing and service method 1100 mayinclude specification and design 1102 of aircraft 1200 in FIG. 12 andmaterial procurement 1104.

During production, component and subassembly manufacturing 1106 andsystem integration 1108 of aircraft 1200 in FIG. 12 takes place.Thereafter, aircraft 1200 in FIG. 12 can go through certification anddelivery 1110 in order to be placed in service 1112. While in service1112 by a customer, aircraft 1200 in FIG. 12 is scheduled for routinemaintenance and service 1114, which may include modification,reconfiguration, refurbishment, and other maintenance or service.

Each of the processes of aircraft manufacturing and service method 1100may be performed or carried out by a system integrator, a third party,an operator, or some combination thereof. In these examples, theoperator may be a customer. For the purposes of this description, asystem integrator may include, without limitation, any number ofaircraft manufacturers and major-system subcontractors; a third partymay include, without limitation, any number of vendors, subcontractors,and suppliers; and an operator may be an airline, a leasing company, amilitary entity, a service organization, and so on.

With reference now to FIG. 12 , an illustration of an aircraft isdepicted in which an illustrative embodiment may be implemented. In thisexample, aircraft 1200 is produced by aircraft manufacturing and servicemethod 1100 in FIG. 11 and may include airframe 1202 with plurality ofsystems 1204 and interior 1206. Examples of systems 1204 include one ormore of propulsion system 1208, electrical system 1210, hydraulic system1212, and environmental system 1214. Any number of other systems may beincluded. Although an aerospace example is shown, different illustrativeembodiments may be applied to other industries, such as the automotiveindustry.

Apparatuses and methods embodied herein may be employed during at leastone of the stages of aircraft manufacturing and service method 1100 inFIG. 11 .

In one illustrative example, components or subassemblies produced incomponent and subassembly manufacturing 1106 in FIG. 11 can befabricated or manufactured in a manner similar to components orsubassemblies produced while aircraft 1200 is in service 1112 in FIG. 11. As yet another example, one or more apparatus embodiments, methodembodiments, or a combination thereof can be utilized during productionstages, such as component and subassembly manufacturing 1106 and systemintegration 1108 in FIG. 11 . One or more apparatus embodiments, methodembodiments, or a combination thereof may be utilized while aircraft1200 is in service 1112, during maintenance and service 1114 in FIG. 11, or both. The use of a number of the different illustrative embodimentsmay substantially expedite the assembly of aircraft 1200, reduce thecost of aircraft 1200, or both expedite the assembly of aircraft 1200and reduce the cost of aircraft 1200.

For example, maintenance manager 212 in FIG. 2 can be used during inservice 1112 and maintenance and service 1114 to predict whenmaintenance should be performed for aircraft 1200. Using machinelearning models 218 to make predictions about components in a mannerthat provides knowledge of impending changes in component performancethat result in undesired aircraft performance. With these predictions,components can be procured ahead of time and the allocation of resourcesfor service and repair can be made more efficiently.

Turning now to FIG. 13 , an illustration of a block diagram of a productmanagement system is depicted in accordance with an illustrativeembodiment. Product management system 1300 is a physical hardwaresystem. In this illustrative example, product management system 1300includes at least one of manufacturing system 1302 or maintenance system1304.

Manufacturing system 1302 is configured to manufacture products, such asaircraft 1200 in FIG. 12 . As depicted, manufacturing system 1302includes manufacturing equipment 1306. Manufacturing equipment 1306includes at least one of fabrication equipment 1308 or assemblyequipment 1310.

Fabrication equipment 1308 is equipment that used to fabricatecomponents for parts used to form aircraft 1200 in FIG. 12 . Forexample, fabrication equipment 1308 can include machines and tools.These machines and tools can be at least one of a drill, a hydraulicpress, a furnace, an autoclave, a mold, a composite tape laying machine,an automated fiber placement (AFP) machine, a vacuum system, a roboticpick and place system, a flatbed cutting machine, a laser cutter, acomputer numerical control (CNC) cutting machine, a lathe, or othersuitable types of equipment. Fabrication equipment 1308 can be used tofabricate at least one of metal parts, composite parts, semiconductors,circuits, fasteners, ribs, skin panels, spars, antennas, or othersuitable types of parts.

Assembly equipment 1310 is equipment used to assemble parts to formaircraft 1200 in FIG. 12 . In particular, assembly equipment 1310 isused to assemble components and parts to form aircraft 1200 in FIG. 12 .Assembly equipment 1310 also can include machines and tools. Thesemachines and tools may be at least one of a robotic arm, a crawler, afaster installation system, a rail-based drilling system, or a robot.Assembly equipment 1310 can be used to assemble parts such as seats,horizontal stabilizers, wings, engines, engine housings, landing gearsystems, and other parts for aircraft 1200 in FIG. 12 .

In this illustrative example, maintenance system 1304 includesmaintenance equipment 1312. Maintenance equipment 1312 can include anyequipment needed to perform maintenance on aircraft 1200 in FIG. 12 .Maintenance equipment 1312 may include tools for performing differentoperations on parts on aircraft 1200 in FIG. 12 . These operations caninclude at least one of disassembling parts, refurbishing parts,inspecting parts, reworking parts, manufacturing replacement parts, orother operations for performing maintenance on aircraft 1200 in FIG. 12. These operations can be for routine maintenance, inspections,upgrades, refurbishment, or other types of maintenance operations.

In the illustrative example, maintenance equipment 1312 may includeultrasonic inspection devices, x-ray imaging systems, vision systems,drills, crawlers, and other suitable devices. In some cases, maintenanceequipment 1312 can include fabrication equipment 1308, assemblyequipment 1310, or both to produce and assemble parts that needed formaintenance.

Product management system 1300 also includes control system 1314.Control system 1314 is a hardware system and may also include softwareor other types of components. Control system 1314 is configured tocontrol the operation of at least one of manufacturing system 1302 ormaintenance system 1304. In particular, control system 1314 can controlthe operation of at least one of fabrication equipment 1308, assemblyequipment 1310, or maintenance equipment 1312.

The hardware in control system 1314 can be implemented using hardwarethat may include computers, circuits, networks, and other types ofequipment. The control may take the form of direct control ofmanufacturing equipment 1306. For example, robots, computer-controlledmachines, and other equipment can be controlled by control system 1314.In other illustrative examples, control system 1314 can manageoperations performed by human operators 1316 in manufacturing orperforming maintenance on aircraft 1200. For example, control system1314 can assign tasks, provide instructions, display models, or performother operations to manage operations performed by human operators 1316.In these illustrative examples, maintenance manager 134 from FIG. 1 canbe implemented in control system 1314 to manage at least one of themanufacturing or maintenance of aircraft 1200 in FIG. 12 .

In the different illustrative examples, human operators 1316 can operateor interact with at least one of manufacturing equipment 1306,maintenance equipment 1312, or control system 1314. This interaction canoccur to manufacture aircraft 1200 in FIG. 12 .

Of course, product management system 1300 may be configured to manageother products other than aircraft 1200 in FIG. 12 . Although productmanagement system 1300 has been described with respect to manufacturingin the aerospace industry, product management system 1300 can beconfigured to manage products for other industries. For example, productmanagement system 1300 can be configured to manufacture products for theautomotive industry as well as any other suitable industries.

Some features of the illustrative examples are described in thefollowing clauses. These clauses are examples of features not intendedto limit other illustrative examples.

Clause 1

A method for managing a maintenance for a component in a vehicle, themethod comprising:

-   -   training behavior machine learning models using first training        data to output predicted values for target parameters for a        normal behavior of the component operating in a tolerance,        wherein each behavior machine learning model in the behavior        machine learning models predicts a target parameter in the        target parameters for the component;    -   determining historical prediction metrics from the predicted        values for the target parameters predicted by the behavior        machine learning models in response to receiving historical        sensor data and actual values for the target parameters for the        component;    -   training a maintenance machine learning model using second        training data to predict whether the maintenance is needed for        the component, wherein the second training data comprises the        historical prediction metrics determined for the target        parameters, wherein the maintenance machine learning model        outputs a prediction as to whether the component will become out        of the tolerance after a time period; and    -   determining whether the maintenance is needed for the component        using sensor data for the component, prediction metrics        determined from the predicted values for the target parameters        output by the behavior machine learning models and the actual        values for the target parameters, and the maintenance machine        learning model.

Clause 2

The method according to clause 1, wherein the first training data for abehavior machine learning model in the behavior machine learning modelscomprises the historical sensor data that has been labeled with theactual values for the target parameters to be predicted by the behaviormachine learning model, wherein each behavior machine learning model istrained to predict a different target parameter from other behaviormachine learning models.

Clause 3

The method according to one of clauses 1 or 2, wherein the firsttraining data is based on at least one of parameters, the targetparameters, or features derived from the historical sensor data for thetarget parameters.

Clause 4

The method according to one of clauses 1, 2, or 3, wherein the secondtraining data comprises the historical prediction metrics and labelsindicating whether the component was out of the tolerance.

Clause 5

The method according to one of clauses 1, 2, 3, or 4, whereindetermining whether the maintenance is needed for the componentcomprises:

-   -   sending sensor data for the component into the behavior machine        learning models;    -   receiving the predicted values for the target parameters from        the behavior machine learning models;    -   determining the prediction metrics from the predicted values for        the target parameters output from the behavior machine learning        model for the sensor data and the actual values for the target        parameters in the sensor data;    -   sending the prediction metrics into the maintenance machine        learning model; and    -   receiving a prediction of whether the maintenance is needed for        the component from the maintenance machine learning model.

Clause 6

The method according to one of clauses 1, 2, 3, 4, or 5, wherein thehistorical prediction metrics are selected from at least one of aprediction error, an absolute prediction error, a root mean squareerror, mean absolute error, or a goodness-of-fit for each targetparameter in the target parameters.

Clause 7

The method according to one of clauses 1, 2, 3, 4, 5, or 6, wherein thebehavior machine learning models are regression machine learning modelsand the maintenance machine learning model is a classification machinelearning model.

Clause 8

The method according to one of clauses 1, 2, 3, 4, 5, 6, or 7, whereinthe vehicle is an aircraft and predictions made using the behaviormachine learning models and the maintenance machine learning model arefor at least one of a phase of flight, a taxi out, a takeoff, a climb, acruise, a descent, a landing, an entire flight of the aircraft, a modeof operation of the aircraft, of a target range of control condition forthe aircraft.

Clause 9

The method according to one of clauses 1, 2, 3, 4, 5, 6, 7, or 8,wherein the component in a vehicle is selected from a mobile platform,an aircraft, a commercial airplane, a tilt-rotor aircraft, a tilt wingaircraft, a vertical takeoff and landing aircraft, an electricalvertical takeoff and landing vehicle, a personal air vehicle, a surfaceship, a tank, a personnel carrier, a train, a spacecraft, a submarine, abus, and an automobile.

Clause 10

A method for managing a maintenance for a component in a vehicle, themethod comprising:

-   -   receiving sensor data for the vehicle;    -   predicting target parameters using behavior machine learning        models trained using first training data to predict the target        parameters for a normal behavior of the component operating in a        tolerance, wherein the target parameters characterize behavior        of the component;    -   determining prediction metrics from predicted values for the        target parameters predicted by behavior machine learning models        and actual values for the target parameters; and    -   predicting whether the component will fall out of the tolerance        after a time period using the prediction metrics and a        maintenance machine learning model trained using second training        data to predict whether the maintenance is needed for the        component, wherein the second training data comprises historical        prediction metrics determined for the target parameters.

Clause 11

A vehicle management system comprising:

-   -   a computer system; and    -   a maintenance manager in the computer system, wherein the        maintenance manager is configured to:    -   train behavior machine learning models using first training data        to output predicted values for target parameters for a normal        behavior of a component operating in a tolerance, wherein each        behavior machine learning model in the behavior machine learning        models predicts a target parameter in the target parameters for        the component;    -   determine historical prediction metrics from the predicted        values for the target parameters predicted by behavior machine        learning models in response to receiving historical sensor data        and actual values for the target parameters for the component;    -   train a maintenance machine learning model using second training        data to predict whether a maintenance is needed for the        component, wherein the second training data comprises the        historical prediction metrics determined for the target        parameters, wherein the maintenance machine learning model        outputs a prediction as to whether the component will become out        of the tolerance after a time period; and    -   determine whether the maintenance is needed for the component        using sensor data for the component, prediction metrics        determined from the predicted values for the target parameters        output by the behavior machine learning models and the actual        values for the target parameters, and the maintenance machine        learning model.

Clause 12

The vehicle management system according to clause 11, wherein the firsttraining data for a behavior machine learning model in the behaviormachine learning models comprises the historical sensor data that hasbeen labeled with the actual values for the target parameters to bepredicted by the behavior machine learning model, wherein each behaviormachine learning model is trained to predict a different targetparameter from other behavior machine learning models.

Clause 13

The vehicle management system according to one of clauses 11 or 12,wherein the first training data comprises at least one of parameters,the target parameters, or features derived from the historical sensordata for the target parameters.

Clause 14

The vehicle management system according to one of clauses 11, 12, or 13,wherein the second training data comprises the prediction metrics andlabels indicating whether the component was out of the tolerance.

Clause 15

The vehicle management system according to one of clauses 11, 12, 13, or14, wherein determining whether the maintenance is needed for thecomponent, the maintenance manager is configured to:

-   -   send sensor data for the component into the behavior machine        learning models;    -   receive the predicted values for the target parameters from the        behavior machine learning models;    -   determine the prediction metrics from the predicted values for        the target parameters output from the behavior machine learning        model for the sensor data and the actual values for the target        parameters in the sensor data;    -   send the prediction metrics into the maintenance machine        learning model; and    -   receive a prediction of whether the maintenance is needed for        the component from the maintenance machine learning model.

Clause 16

The vehicle management system according to one of clauses 11, 12, 13,14, or 15, wherein the prediction metrics are selected from at least oneof a prediction error, an absolute prediction error, a root mean squareerror, mean absolute error, or a goodness-of-fit for each targetparameter in the target parameters.

Clause 17

The vehicle management system according to one of clauses 11, 12, 13,14, 15, or 16, wherein the behavior machine learning models areregression machine learning models and the maintenance machine learningmodel is a classification machine learning model.

Clause 18

The vehicle management system according to one of clauses 11, 12, 13,14, 15, 16, or 17, wherein a vehicle is an aircraft and predictions madefor using the behavior machine learning models and the maintenancemachine learning model are for at least one of a phase of flight, a taxiout, a takeoff, a climb, a cruise, a descent, a landing, or an entireflight of the aircraft.

Clause 19

The vehicle management system according to one of clauses 11, 12, 13,14, 15, 16, 17, or 18, wherein the component in a vehicle is selectedfrom a mobile platform, an aircraft, a commercial airplane, a tilt-rotoraircraft, a tilt wing aircraft, a vertical takeoff and landing aircraft,an electrical vertical takeoff and landing vehicle, a personal airvehicle, a surface ship, a tank, a personnel carrier, a train, aspacecraft, a submarine, a bus, and an automobile.

Clause 20

A vehicle management system comprising:

-   -   a computer system; and    -   a maintenance manager in the computer system, wherein the        maintenance manager is configured to:    -   receive sensor data for a vehicle;    -   predict target parameters using behavior machine learning models        trained using first training data to predict the target        parameters for a normal behavior of a component operating in a        tolerance, wherein the target parameters characterize behavior        of the component;    -   determine prediction metrics from predicted values for the        target parameters predicted by the behavior machine learning        models and actual values for the target parameters; and    -   predict whether the component will fall out of the tolerance        after a time period using the prediction metrics and a        maintenance machine learning model trained using second training        data to predict whether a maintenance is needed for the        component, wherein the second training data comprises historical        prediction metrics determined for the target parameters and the        actual values for the target parameters.

Illustrative examples provide a method, apparatus, system, and computerprogram product for managing maintenance of components. In theillustrative examples, a maintenance manager can train behavior machinelearning models using first training data to predict target parameters.The first training data can comprise historical sensor data labeled withactual values for target parameters.

The target parameters predicted can be used to train a maintenancemachine learning model. In this illustrative example, the predictedtarget parameters can be used to determine prediction metrics that formtraining data. The second training data is used to train a machinelearning model to predict when the component will fall out of tolerance.

In this illustrative example, sensor data from a use of a vehicle can beinput into the behavior machine learning models trained using the firsttraining data. In response, these behavior machine learning modelsoutput predictions of the target parameters. These predictions can beused to determine prediction metrics which are then sent into themaintenance machine learning model as an input. In response, themaintenance machine learning model outputs the prediction of whencomponent will fall out of tolerance. For example, the prediction can bewhether the component will fall out of tolerance in the next five days.

Thus, illustrative example provides a multistage data-driven approach togenerate predictions for use in performing maintenance on a vehicle suchas aircraft. The illustrative examples can handle large amounts of dataand reduce the amount of data processed based on at least one of aselective target parameters for features derived from parameters. Asresult, with the selection of at least one of target parameters orfeature creation, the use of processor resources can be reduced whileincreasing the amount of data that is used for training.

Further, the use of prediction metrics can be used to consider differenterror statistics for different conditions, such as phase of flight,environmental conditions, or other conditions.

The description of the different illustrative embodiments has beenpresented for purposes of illustration and description and is notintended to be exhaustive or limited to the embodiments in the formdisclosed. The different illustrative examples describe components thatperform actions or operations. In an illustrative embodiment, acomponent can be configured to perform the action or operationdescribed. For example, the component can have a configuration or designfor a structure that provides the component an ability to perform theaction or operation that is described in the illustrative examples asbeing performed by the component. Further, To the extent that terms“includes”, “including”, “has”, “contains”, and variants thereof areused herein, such terms are intended to be inclusive in a manner similarto the term “comprises” as an open transition word without precludingany additional or other elements.

Many modifications and variations will be apparent to those of ordinaryskill in the art. Further, different illustrative embodiments mayprovide different features as compared to other desirable embodiments.The embodiment or embodiments selected are chosen and described in orderto best explain the principles of the embodiments, the practicalapplication, and to enable others of ordinary skill in the art tounderstand the disclosure for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A method for managing a maintenance for acomponent in a vehicle, the method comprising: training behavior machinelearning models using first training data to output predicted values fortarget parameters for a normal behavior of the component operating in atolerance, wherein each behavior machine learning model in the behaviormachine learning models predicts a target parameter in the targetparameters for the component; determining historical prediction metricsfrom the predicted values for the target parameters predicted by thebehavior machine learning models in response to receiving historicalsensor data and actual values for the target parameters for thecomponent; training a maintenance machine learning model using secondtraining data to predict whether the maintenance is needed for thecomponent, wherein the second training data comprises the historicalprediction metrics determined for the target parameters, wherein themaintenance machine learning model outputs a prediction as to whetherthe component will become out of the tolerance after a time period; anddetermining whether the maintenance is needed for the component usingsensor data for the component, prediction metrics determined from thepredicted values for the target parameters output by the behaviormachine learning models and the actual values for the target parameters,and the maintenance machine learning model.
 2. The method of claim 1,wherein the first training data for a behavior machine learning model inthe behavior machine learning models comprises the historical sensordata that has been labeled with the actual values for the targetparameters to be predicted by the behavior machine learning model,wherein each behavior machine learning model is trained to predict adifferent target parameter from other behavior machine learning models.3. The method of claim 1, wherein the first training data is based on atleast one of parameters, the target parameters, or features derived fromthe historical sensor data for the target parameters.
 4. The method ofclaim 1, wherein the second training data comprises the historicalprediction metrics and labels indicating whether the component was outof the tolerance.
 5. The method of claim 1, wherein determining whetherthe maintenance is needed for the component comprises: sending sensordata for the component into the behavior machine learning models;receiving the predicted values for the target parameters from thebehavior machine learning models; determining the prediction metricsfrom the predicted values for the target parameters output from thebehavior machine learning model for the sensor data and the actualvalues for the target parameters in the sensor data; sending theprediction metrics into the maintenance machine learning model; andreceiving a prediction of whether the maintenance is needed for thecomponent from the maintenance machine learning model.
 6. The method ofclaim 1, wherein the historical prediction metrics are selected from atleast one of a prediction error, an absolute prediction error, a rootmean square error, mean absolute error, or a goodness-of-fit for eachtarget parameter in the target parameters.
 7. The method of claim 1,wherein the behavior machine learning models are regression machinelearning models and the maintenance machine learning model is aclassification machine learning model.
 8. The method of claim 1, whereinthe vehicle is an aircraft and predictions made using the behaviormachine learning models and the maintenance machine learning model arefor at least one of a phase of flight, a taxi out, a takeoff, a climb, acruise, a descent, a landing, an entire flight of the aircraft, a modeof operation of the aircraft, of a target range of control condition forthe aircraft.
 9. The method of claim 1, wherein the component in avehicle is selected from a mobile platform, an aircraft, a commercialairplane, a tilt-rotor aircraft, a tilt wing aircraft, a verticaltakeoff and landing aircraft, an electrical vertical takeoff and landingvehicle, a personal air vehicle, a surface ship, a tank, a personnelcarrier, a train, a spacecraft, a submarine, a bus, and an automobile.10. A method for managing a maintenance for a component in a vehicle,the method comprising: receiving sensor data for the vehicle; predictingtarget parameters using behavior machine learning models trained usingfirst training data to predict the target parameters for a normalbehavior of the component operating in a tolerance, wherein the targetparameters characterize behavior of the component; determiningprediction metrics from predicted values for the target parameterspredicted by behavior machine learning models and actual values for thetarget parameters; and predicting whether the component will fall out ofthe tolerance after a time period using the prediction metrics and amaintenance machine learning model trained using second training data topredict whether the maintenance is needed for the component, wherein thesecond training data comprises historical prediction metrics determinedfor the target parameters.
 11. A vehicle management system comprising: acomputer system; and a maintenance manager in the computer system,wherein the maintenance manager is configured to: train behavior machinelearning models using first training data to output predicted values fortarget parameters for a normal behavior of a component operating in atolerance, wherein each behavior machine learning model in the behaviormachine learning models predicts a target parameter in the targetparameters for the component; determine historical prediction metricsfrom the predicted values for the target parameters predicted bybehavior machine learning models in response to receiving historicalsensor data and actual values for the target parameters for thecomponent; train a maintenance machine learning model using secondtraining data to predict whether a maintenance is needed for thecomponent, wherein the second training data comprises the historicalprediction metrics determined for the target parameters, wherein themaintenance machine learning model outputs a prediction as to whetherthe component will become out of the tolerance after a time period; anddetermine whether the maintenance is needed for the component usingsensor data for the component, prediction metrics determined from thepredicted values for the target parameters output by the behaviormachine learning models and the actual values for the target parameters,and the maintenance machine learning model.
 12. The vehicle managementsystem of claim 11, wherein the first training data for a behaviormachine learning model in the behavior machine learning models comprisesthe historical sensor data that has been labeled with the actual valuesfor the target parameters to be predicted by the behavior machinelearning model, wherein each behavior machine learning model is trainedto predict a different target parameter from other behavior machinelearning models.
 13. The vehicle management system of claim 11, whereinthe first training data comprises at least one of parameters, the targetparameters, or features derived from the historical sensor data for thetarget parameters.
 14. The vehicle management system of claim 11,wherein the second training data comprises the prediction metrics andlabels indicating whether the component was out of the tolerance. 15.The vehicle management system of claim 11, wherein determining whetherthe maintenance is needed for the component, the maintenance manager isconfigured to: send sensor data for the component into the behaviormachine learning models; receive the predicted values for the targetparameters from the behavior machine learning models; determine theprediction metrics from the predicted values for the target parametersoutput from the behavior machine learning model for the sensor data andthe actual values for the target parameters in the sensor data; send theprediction metrics into the maintenance machine learning model; andreceive a prediction of whether the maintenance is needed for thecomponent from the maintenance machine learning model.
 16. The vehiclemanagement system of claim 11, wherein the prediction metrics areselected from at least one of a prediction error, an absolute predictionerror, a root mean square error, mean absolute error, or agoodness-of-fit for each target parameter in the target parameters. 17.The vehicle management system of claim 11, wherein the behavior machinelearning models are regression machine learning models and themaintenance machine learning model is a classification machine learningmodel.
 18. The vehicle management system of claim 11, wherein a vehicleis an aircraft and predictions made for using the behavior machinelearning models and the maintenance machine learning model are for atleast one of a phase of flight, a taxi out, a takeoff, a climb, acruise, a descent, a landing, or an entire flight of the aircraft. 19.The vehicle management system of claim 11, wherein the component in avehicle is selected from a mobile platform, an aircraft, a commercialairplane, a tilt-rotor aircraft, a tilt wing aircraft, a verticaltakeoff and landing aircraft, an electrical vertical takeoff and landingvehicle, a personal air vehicle, a surface ship, a tank, a personnelcarrier, a train, a spacecraft, a submarine, a bus, and an automobile.20. A vehicle management system comprising: a computer system; and amaintenance manager in the computer system, wherein the maintenancemanager is configured to: receive sensor data for a vehicle; predicttarget parameters using behavior machine learning models trained usingfirst training data to predict the target parameters for a normalbehavior of a component operating in a tolerance, wherein the targetparameters characterize behavior of the component; determine predictionmetrics from predicted values for the target parameters predicted by thebehavior machine learning models and actual values for the targetparameters; and predict whether the component will fall out of thetolerance after a time period using the prediction metrics and amaintenance machine learning model trained using second training data topredict whether a maintenance is needed for the component, wherein thesecond training data comprises historical prediction metrics determinedfor the target parameters and the actual values for the targetparameters.